Application of Regression Modeling to Data Observed Over Time

Detalhes bibliográficos
Autor(a) principal: Figueiredo, Cléber da Costa
Data de Publicação: 2018
Outros Autores: Silva, Aldy Fernandes da
Tipo de documento: Artigo
Idioma: por
eng
Título da fonte: Internext
Texto Completo: https://internext.espm.br/internext/article/view/477
Resumo: The central idea of this text is to guide researchers through the application of regression modeling when the data under analysis are observed over time. In general, there are no doubts regarding the application of this modeling in cross sections. However, when there is dependence on the data over time, some care needs to be taken for the results to be reliable and have the same interpretation of the coefficients obtained using the least squares method. The text begins with a presentation of the concept of autocorrelation and partial autocorrelation to identify and apply autoregressive modeling. Following this approach, the Augmented Dickey-Fuller test for detecting stationarity is presented, an essential condition for the estimators of ordinary least squares to be consistent. The Granger causality test is also presented and an example of regression applied to the series of the Cost of Living Index and the National Price Index for General Consumers. All the examples are presented with the help of Microsoft Excel to universalize the technique.
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spelling Application of Regression Modeling to Data Observed Over TimeAplicação da Modelagem de Regressão em Dados Observados ao Longo do TempoLongitudinal dataStationarityAutoregressive modelsGranger causalityLagDados longitudinaisEstacionariedadeModelos autorregressivosCausalidade de GrangerDefasagemThe central idea of this text is to guide researchers through the application of regression modeling when the data under analysis are observed over time. In general, there are no doubts regarding the application of this modeling in cross sections. However, when there is dependence on the data over time, some care needs to be taken for the results to be reliable and have the same interpretation of the coefficients obtained using the least squares method. The text begins with a presentation of the concept of autocorrelation and partial autocorrelation to identify and apply autoregressive modeling. Following this approach, the Augmented Dickey-Fuller test for detecting stationarity is presented, an essential condition for the estimators of ordinary least squares to be consistent. The Granger causality test is also presented and an example of regression applied to the series of the Cost of Living Index and the National Price Index for General Consumers. All the examples are presented with the help of Microsoft Excel to universalize the technique.A ideia central deste texto é orientar o pesquisador a aplicar a modelagem de regressão quando os dados em análise foram observados ao longo do tempo. Em geral, não há dúvidas da aplicação dessa modelagem em seções transversais. Contudo, quando há dependência dos dados ao longo do tempo, alguns cuidados precisam ser tomados para que os resultados sejam confiáveis e valham as mesmas interpretações dos coeficientes obtidos via o método de mínimos quadrados. O texto inicia com a apresentação do conceito de autocorrelação e de autocorrelação parcial, a fim de identificar e aplicar a modelagem autorregressiva. Após essa abordagem, é apresentado o teste de Dickey-Fuller Aumentado para a detecção de estacionariedade, condição essencial para que os estimadores de mínimos quadrados ordinários sejam consistentes. Também é apresentado o teste de causalidade de Granger e um exemplo de regressão aplicado às séries do Índice de Custo de Vida e Índice de Preços ao Consumidor Amplo. Todos os exemplos foram apresentados com a ajuda do Microsoft Excel, a fim de universalizar a técnica.Escola Superior de Propaganda e Marketing - ESPM2018-09-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://internext.espm.br/internext/article/view/47710.18568/1980-4865.13342-50Internext - International Business and Management Review ; Vol. 13 No. 3 (2018): September/December; 42-50Internext; v. 13 n. 3 (2018): Setembro/Dezembro; 42-501980-4865reponame:Internextinstname:Escola Superior de Propaganda e Marketing (ESPM)instacron:ESPMporenghttps://internext.espm.br/internext/article/view/477/pdfhttps://internext.espm.br/internext/article/view/477/pdf_1Copyright (c) 2018 Internextinfo:eu-repo/semantics/openAccessFigueiredo, Cléber da CostaSilva, Aldy Fernandes da2023-06-06T20:47:27Zoai:ojs.emnuvens.com.br:article/477Revistahttps://internext.espm.br/internextPRIhttps://internext.espm.br/internext/oaiinternext@espm.br1980-48651980-4865opendoar:2023-06-06T20:47:27Internext - Escola Superior de Propaganda e Marketing (ESPM)false
dc.title.none.fl_str_mv Application of Regression Modeling to Data Observed Over Time
Aplicação da Modelagem de Regressão em Dados Observados ao Longo do Tempo
title Application of Regression Modeling to Data Observed Over Time
spellingShingle Application of Regression Modeling to Data Observed Over Time
Figueiredo, Cléber da Costa
Longitudinal data
Stationarity
Autoregressive models
Granger causality
Lag
Dados longitudinais
Estacionariedade
Modelos autorregressivos
Causalidade de Granger
Defasagem
title_short Application of Regression Modeling to Data Observed Over Time
title_full Application of Regression Modeling to Data Observed Over Time
title_fullStr Application of Regression Modeling to Data Observed Over Time
title_full_unstemmed Application of Regression Modeling to Data Observed Over Time
title_sort Application of Regression Modeling to Data Observed Over Time
author Figueiredo, Cléber da Costa
author_facet Figueiredo, Cléber da Costa
Silva, Aldy Fernandes da
author_role author
author2 Silva, Aldy Fernandes da
author2_role author
dc.contributor.author.fl_str_mv Figueiredo, Cléber da Costa
Silva, Aldy Fernandes da
dc.subject.por.fl_str_mv Longitudinal data
Stationarity
Autoregressive models
Granger causality
Lag
Dados longitudinais
Estacionariedade
Modelos autorregressivos
Causalidade de Granger
Defasagem
topic Longitudinal data
Stationarity
Autoregressive models
Granger causality
Lag
Dados longitudinais
Estacionariedade
Modelos autorregressivos
Causalidade de Granger
Defasagem
description The central idea of this text is to guide researchers through the application of regression modeling when the data under analysis are observed over time. In general, there are no doubts regarding the application of this modeling in cross sections. However, when there is dependence on the data over time, some care needs to be taken for the results to be reliable and have the same interpretation of the coefficients obtained using the least squares method. The text begins with a presentation of the concept of autocorrelation and partial autocorrelation to identify and apply autoregressive modeling. Following this approach, the Augmented Dickey-Fuller test for detecting stationarity is presented, an essential condition for the estimators of ordinary least squares to be consistent. The Granger causality test is also presented and an example of regression applied to the series of the Cost of Living Index and the National Price Index for General Consumers. All the examples are presented with the help of Microsoft Excel to universalize the technique.
publishDate 2018
dc.date.none.fl_str_mv 2018-09-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://internext.espm.br/internext/article/view/477
10.18568/1980-4865.13342-50
url https://internext.espm.br/internext/article/view/477
identifier_str_mv 10.18568/1980-4865.13342-50
dc.language.iso.fl_str_mv por
eng
language por
eng
dc.relation.none.fl_str_mv https://internext.espm.br/internext/article/view/477/pdf
https://internext.espm.br/internext/article/view/477/pdf_1
dc.rights.driver.fl_str_mv Copyright (c) 2018 Internext
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2018 Internext
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Escola Superior de Propaganda e Marketing - ESPM
publisher.none.fl_str_mv Escola Superior de Propaganda e Marketing - ESPM
dc.source.none.fl_str_mv Internext - International Business and Management Review ; Vol. 13 No. 3 (2018): September/December; 42-50
Internext; v. 13 n. 3 (2018): Setembro/Dezembro; 42-50
1980-4865
reponame:Internext
instname:Escola Superior de Propaganda e Marketing (ESPM)
instacron:ESPM
instname_str Escola Superior de Propaganda e Marketing (ESPM)
instacron_str ESPM
institution ESPM
reponame_str Internext
collection Internext
repository.name.fl_str_mv Internext - Escola Superior de Propaganda e Marketing (ESPM)
repository.mail.fl_str_mv internext@espm.br
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